isp-uv-es/wildfires-cems-taco

Wildfires - CEMS

This is a repackaging, not a new dataset. It is Wildfires - CEMS by Copernicus Emergency Management Service, LINKS Foundation / Politecnico di Torino (Arnaudo et al.), converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@inproceedings{arnaudo2023burned,
  title     = {Robust Burned Area Delineation Through Multitask Learning},
  author    = {Arnaudo, Edoardo and Barco, Luca and Merlo, Matteo and Rossi, Claudio},
  booktitle = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023 Workshops)},
  series    = {Communications in Computer and Information Science},
  publisher = {Springer},
  pages     = {436--447},
  year      = {2025},
  doi       = {10.1007/978-3-031-74633-8_32}
}

About the data

Burned-area delineation and damage grading over European wildfires: 433 Sentinel-2 L2A tiles (12 bands, surface reflectance as float) across 171 Copernicus EMS rapid-mapping activations.

433 samples · splits: test 99 · train 281 · validation 53 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Targets. Each tile carries the CEMS burned-area delineation, the CEMS damage grading (5 ordinal levels), an ESA WorldCover land-cover map and a CloudSEN12 cloud mask with its four classes (clear, thick cloud, thin cloud, cloud shadow). The release's README calls the cloud mask binary; the pixels are not.

Splits. The release's own, keeping each activation whole.

Getting started

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

import os
from huggingface_hub import hf_hub_download, snapshot_download
from taco.ml import Dataset, plot_sample

path = hf_hub_download("isp-uv-es/wildfires-cems-taco", "wildfires-cems.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

ds = Dataset("wildfires-cems.zip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["s2"].array.shape

Metadata without decoding anything:

import taco
taco.read("wildfires-cems.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	s2	raster	multispectral	12 band(s), unit 1, physical
input	land_cover	mask	label_raster	11 classes
input	cloud	mask	label_raster	4 classes
target	delineation	mask	label_raster	2 classes
target	grading	mask	label_raster	5 classes

Licence

CC-BY-4.0

Providers: Copernicus Emergency Management Service, LINKS Foundation / Politecnico di Torino (Arnaudo et al.)

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.